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Complex network analysis of human ECoG data.

机译:人类ECoG数据的复杂网络分析。

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摘要

Localization of the epileptogenic zone (EZ) is an important issue in epileptology, even though there is not a unique definition of the epileptic focus. By using complex network analysis of electrocorticographic (ECoG) data we identify three singular areas in the temporal lobe of epileptic patients, the node with highest local synchronization power, the most connected node, and the node with highest interactions load. Connectivity in the data is extracted from the Minimum Spanning Tree (MST) of global correlations. We address the question whether removal of these nodes during the surgery is crucial in the suppression or reduction in the quantity of post-operative seizures. From five ECoG records, local areas with high synchronization power appear to be significantly involved in the development of epileptic seizures. The other two areas seem not to be fundamental in the seizures onset and development. Moreover, the approach proposed shed new light in cortical connectivity patterns in the human temporal lobe. All the analyzed records are during the inter-ictal state.
机译:尽管没有关于癫痫病灶的唯一定义,但是癫痫发生区(EZ)的定位是癫痫学中的一个重要问题。通过使用复杂的脑电图(ECoG)数据网络分析,我们确定了癫痫患者颞叶中的三个奇异区域,即本地同步功率最高的节点,连接最多的节点以及交互负荷最高的节点。数据的连通性是从全局相关性的最小生成树(MST)中提取的。我们解决了以下问题:在手术期间移除这些淋巴结是否对抑制或减少术后癫痫发作的数量至关重要。从五项ECoG记录来看,具有较高同步能力的本地地区似乎与癫痫发作的发展密切相关。其他两个方面似乎不是癫痫发作和发展的基础。此外,提出的方法为人类颞叶的皮质连通性模式提供了新的思路。所有分析的记录均处于发作间状态。

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